Adaptive multimodal graph learning for knowledge graph completion

Jie Peng

doi:10.1007/s10618-025-01139-x

2025-08-19

PRE

Jie Peng

Yongfu Zha

Xiaodong Wang *

DOI：10.1007/s10618-025-01139-x

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摘要

En 中文

多模态知识图谱通过整合视觉和文本数据，已被广泛应用于各类应用中。尽管其潜力巨大，但往往因图谱内存在大量未被发现的宝贵三元组知识而面临不完整性的问题。这促使了多模态知识图谱补全研究的激增。然而，现有方法常面临多模态数据中的无关噪声以及简单多模态融合的局限性等挑战，可能导致模型性能欠佳。本文提出了一种新颖的自适应多模态图学习方法，用于高效的知识图谱补全。我们首先引入一个自适应多模态知识捕获模块，旨在整合实体相关的多模态知识。该模块包含一个关系感知的模态视图构建过程，以实现语义意义一致性，同时利用链路感知的点对面交互实现粗粒度多模态捕获，以及自适应点对点交互实现细粒度多模态提取。随后，我们提出一个两阶段多模态图融合模块，其中包括一个跨模态增强模块，用于在实体图与视觉/文本图之间执行第一阶段多模态融合，以及一个动态选择融合模块，用于在实体-视觉图与实体-文本图之间进行第二阶段融合。通过在三个常用数据集上的经验评估，我们的方法展现出卓越的有效性。

Keyword:

Knowledge Graph

Knowledge Graph Completion

Multimodal Learning

Knowledge Discovery

期刊

Data Mining and Knowledge Discovery

IF:

4.3

论文数:

217

被引数:

6.0K

机构

College of Computer Science and Technology

学者数:

854

论文数: 298

被引数: 0

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